Jarvis, D and Kyriacou, T (2018) The Effect of Pose on the distribution of Edge Gradients in Omnidirectional Images. In: Towards Autonomous Robotic Systems. TAROS 2018. Lecture Notes in Computer Science, 10965 . Springer. ISBN 9783319967288

[thumbnail of jarvis_and_kyriacou_2018.pdf]
jarvis_and_kyriacou_2018.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.

Download (6MB) | Preview


Images from omnidirectional cameras are used frequently in applications involving artificial intelligence and robotics as a source of rich information about the surroundings. A useful feature that can be extracted from these images is the distribution of gradients of the edges in the scene. This distribution is affected by the pose of the camera on-board a robot at any given location in the environment. This paper investigates the effect of the pose on this distribution. The gradients in the images are extracted and arranged into a histogram which is then compared to the histograms of other images using a chi-squared test. It is found that any differences in the distribution are not specific to either the position or orientation and that there is a significant difference in the distributions of two separate locations. This can aid in the localisation of robots when navigating.

Item Type: Book Section
Additional Information: This conference paper has been accepted for the forthcoming 19th Towards Autonomous Robotic Systems (TAROS) Conference, Bristol Robotics Laboratory at the University of the West of England 25th - 27th July 2018. It will be published in the series Lecture Notes in Artificial Intelligence, published by Springer.
Uncontrolled Keywords: Jarvis D., Kyriacou T. (2018) The Effect of Pose on the Distribution of Edge Gradients in Omnidirectional Images. In: Giuliani M., Assaf T., Giannaccini M. (eds) Towards Autonomous Robotic Systems. TAROS 2018. Lecture Notes in Computer Science, vol 10965. Springer, Cham
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Natural Sciences > School of Computing and Mathematics
Depositing User: Symplectic
Date Deposited: 07 May 2018 11:46
Last Modified: 21 Jul 2019 01:30
URI: https://eprints.keele.ac.uk/id/eprint/4857

Actions (login required)

View Item
View Item